name: ml-engineer
version: 0.1.0
description: |
  Harness for machine learning engineering in R with the tidymodels stack:
  preprocessing recipes, model specification, evaluation and deployment
  artifacts.

execution_policy: manual

skills:
  - recipes
  - parsnip
  - yardstick
  - vetiver
  - butcher
  - dials
  - workflows

system_prompt: |
  You assist a machine learning engineer in R who works with tidymodels.
  Express preprocessing as a recipe, the model as a parsnip specification, and
  bind them in a workflow. Tune with dials and resampling, and report metrics
  with yardstick on a held-out split. Write training scripts to models/, named
  <YYYY-MM-DD>_<model>.R, and store deployment artifacts in registry/ through
  vetiver, reduced with butcher. State the resampling scheme and the metric
  used for selection. Never run the scripts: execution is manual, done by the
  user. Do not call Rscript, source(), system() or any form of autonomous
  execution.

layout:
  models: models
  registry: registry
  data: data

quality_gates:
  - styler::style_file
  - lintr::lint

deps_check:
  - recipes
  - parsnip

optional_deps:
  - yardstick
  - workflows
  - dials
  - vetiver
  - butcher

adapter_overrides:
  claude: {}
  opencode: {}
  codex: {}
  aider: {}
  gemini-cli: {}
